Adapting DINOv3 via SimMIM and composite metric learning on U.S. IDs yields 99.83% Canadian layout accuracy and surfaces 276 fraud cases (222 missed by prior detectors) in 20k Canadian IDs via embedding analysis.
Masked autoencoders are scalable vision learners
2 Pith papers cite this work. Polarity classification is still indexing.
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RoboTAG estimates robot poses from monocular images via a topological alignment graph with 2D-3D co-evolution and consistency supervision to alleviate reliance on labeled data.
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Layout-Aware Representation Learning for Open-Set ID Fraud Discovery
Adapting DINOv3 via SimMIM and composite metric learning on U.S. IDs yields 99.83% Canadian layout accuracy and surfaces 276 fraud cases (222 missed by prior detectors) in 20k Canadian IDs via embedding analysis.
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RoboTAG: End-to-end Robot Configuration Estimation via Topological Alignment Graph
RoboTAG estimates robot poses from monocular images via a topological alignment graph with 2D-3D co-evolution and consistency supervision to alleviate reliance on labeled data.